Lecture 6: Data Exchange and Semantic Annotation

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1 Lecture 6: Data Exchange and Semantic Annotation TIES4520 Semantic Technologies for Developers Autumn 2017 University of Jyväskylä Khriyenko Oleksiy

2 Part 1 Data Exchange 2

3 Machine readable data exchange RDFa Resource Description Framework in attributes (W3C Recommendation). It is a domain-independent way to explicitly embed RDF data in attributes of a web page to: transfer data from an application to another through the web; write data only once for web users and web applications. JSON-LD - JavaScript Object Notation for Linked Data. Extension of JSON - simple property-value type machine readable data exchange format 3

4 RDFa RDFa 1.1 is for XHTML and HTML5, also works for any XML-based languages like SVG. (You can use HTML+RDFa, but it won t be officially valid HTML file) RDFa Lite is a minimal subset of RDFa ( RDFa 1.1 Prime is Rich Structured Data Markup for Web Documents ( RDFa Core 1.1 is complete specification of RDFa ( Useful links: Basic presentation: RDFa materials for users and developers: Real-time RDFa 1.1 editor: RDFa Online Parser: RDFa 1.1 Distiller: 4

RDFa <div vocab= http://schema.org/ prefix= ex: http://example.

much better photos than I do: <div resource="ex:bob/photos/sunset.jpg" prefix= dc: http://purl.org/dc/terms/ > <img src="http://example.

5 RDFa <div vocab= prefix= ex: resource="ex:alice/posts/trouble_with_bob typeof= Article > <h2 property="title">the trouble with Bob</h2> The trouble with Bob is that he takes much better photos than I do: <div resource="ex:bob/photos/sunset.jpg" prefix= dc: > <img src=" /> <span property= title">beautiful Sunset</span> by <span property= dc:creator">bob</span>. </div> </div> The trouble with Bob The trouble with Bob is that he takes much better photos than I do: Beautiful Sunset by Bob 5

6 RDFa <div vocab= prefix= ex: resource="ex:alice/posts/trouble_with_bob typeof= Article > <h2 property="title">the trouble with Bob</h2> The trouble with Bob is that he takes much better photos than I do: <div resource="ex:bob/photos/sunset.jpg" prefix= dc: > <img src=" /> <span property= title">beautiful Sunset</span> by <span property= dc:creator">bob</span>. </div> sc: ex: dc: < ex:alice/posts/trouble_with_bob a sc:article; sc:title The trouble with Bob. ex:bob/photos/sunset.jpg sc:title Beautiful Sunset ; dc:creator Bob. 6

RDFa Lite 1.1 RDFa Lite consists of five simple attributes: vocab (default vocabulary, applied until be redefined), typeof, property, resource and prefix. <div vocab= http://schema.

7 RDFa Lite 1.1 RDFa Lite consists of five simple attributes: vocab (default vocabulary, applied until be redefined), typeof, property, resource and prefix. <div vocab= prefix= ex: resource="ex:alice/posts/trouble_with_bob typeof= Article > <h2 property="title">the trouble with Bob</h2> The trouble with Bob is that he takes much better photos than I do: <div resource="ex:bob/photos/sunset.jpg" prefix= dc: > <img src=" /> <span property= title">beautiful Sunset</span> by <span property= dc:creator">bob</span>. </div> </div> A full list of pre-declared prefixes: sc: ex: dc: < ex:alice/posts/trouble_with_bob a sc:article; sc:title The trouble with Bob. ex:bob/photos/sunset.jpg sc:title Beautiful Sunset ; dc:creator Bob. 7

8 More of RDFa It is possible to define a blank node by named blank node (e.g. _:name ) or just by mentioning a type of the node. If the element contains the href (or src) attribute, property is automatically associated with the value of the attribute rather than the textual content of the <a> element. <div vocab=" <ul> <li typeof="person"> <a property="homepage" href=" </li> <li typeof="person"> <a property="homepage" href=" </li> </ul> </div> <div vocab=" typeof="person"> <span property="name">alice Birpemswick</span>, <ul> <li property="knows" typeof="person"> <a property="homepage" href=" property="name">bob</span></a> </li> <li property="knows" typeof="person"> <a property="homepage" href=" property="name">eve</span></a> </li> </ul> </div> 8

9 More of RDFa HTML+RDFa allows Property copying in case you have repeating set of data. It is possible to collect a number of statements as a pattern (rdfa:pattern) and refer to it using the property rdfa:copy. <body vocab=" <div resource="/alice/posts/trouble_with_bob"> <h2 property="title">the trouble with Bob</h2> /some repeating part/ </div> <div resource="/alice/posts/jims_concert"> <h2 property="title">i was at Jim's concert the other day</h2> /some repeating part/ </div> </body> <body vocab=" <div resource="/alice/posts/trouble_with_bob"> <h2 property="title">the trouble with Bob</h2> <link property="rdfa:copy" href="#cpattern"/> </div> <div resource="/alice/posts/jims_concert"> <h2 property="title">i was at Jim's concert the other day</h2> <link property="rdfa:copy" href="#cpattern"/> </div> <div resource="#cpattern" typeof="rdfa:pattern"> /some repeating part/ </div> </body> 9

10 More of RDFa RDFa allows the value of a property and typeof attributes to be a list of values <div resource="ex:me" typeof="foaf:person schema:person" > <h3 property="dc:creator schema:creator" resource="ex:me">john</h3> </div> Human readability vs. unambiguity for machine readability (RDFa Core) <p>date: <span property=" <p>date: <span property=" th of November, 2016</span></p> RDFa makes it possible to re-use the content attribute of HTML <p>date: <span property=" content=" ">28th of November, 2016</span></p> also, content attribute can be useful when we define some statements through meta element (that may have no text content) in the header of the document <head prefix="og: > <meta property="og:title" content="the Trouble with Bob" /> </head> 10

11 More of RDFa RDFa Core introduces attribute about that can be used as an alternative to resource in setting the context (the subject of the statement) Attribute resource may be used to present subject or object of a statement <div resource="/alice/posts/trouble"> <h2 property="title">the trouble </h2> <h3 property="creator" resource="#me">alice</h3> </div> Example: We need to set up a separate index page for all different blogs <ul resource="/alice/posts/trouble"> <li resource="/alice/posts/trouble" property="title">the trouble </li> <li resource="/alice/posts/jos" property="title">jo s Barbecue</li> </ul> The combination of property and resource inside the same element would be considered as predicate and object and would generate a different statement than originally intended. <ul> <li resource="/alice/posts/trouble"><span property="title">the trouble </span></li> <li resource="/alice/posts/jos"><span property="title">jo's Barbecue</span></li> </ul> Current solution becomes a little bit complicated. Therefore about could be used <ul> <li about="/alice/posts/trouble" property="title">the trouble </li> <li about="/alice/posts/jos" property="title">jo's Barbecue</li> </ul> 11

12 More of RDFa RDFa Core allows definition of datatypes and language tag <span property="dc:date" datatype="xsd:gyear">2011</span> <span property="dc:name" xml:lang="en">john</span> RDFa Core attribute rel can be used as an alternative to property In contrast to property, rel never considers the textual content of an element (or the value of the content attribute). Instead, if no clear target has been specified for a link via, e.g., a resource or an href attribute, the processor is supposed to go down and find one or more targets in the hierarchy and use those. <div vocab=" resource="#me"> <ul> <li property="knows" resource=" typeof="person"> </li> <li property="knows" resource=" typeof="person"> </li> </ul> </div> <div vocab=" resource="#me"> <ul rel="knows"> <li resource=" typeof="person"> </li> <li resource=" typeof="person"> </li> </ul> </div> 12

13 More of RDFa <html xmlns=" prefix= dbp: prefix= dbp-owl: prefix= dbr: prefix= foaf: prefix= xsd: <head> <title>albert Einstein</title> </head> <body> <div about= dbr:albert_einstein"> <span property="foaf:name">albert Einstein</span> <span property="dbp:dateofbirth" datatype="xsd:date"> </span> <div rel="dbp:birthplace" resource="dbp:german_empire"> <span property="dbp:conventionallongname">the German Empire</span> <span rel="dbp-owl:capital" resource="dbr:berlin" /> </div> </div> </body> dbr:albert_einstein foaf:name "Albert Einstein". dbr:albert_einstein dbp:dateofbirth " "^^xsd:date. dbr:albert_einstein dbp:birthplace dbr:german_empire. dbr:german_empire dbp:conventionallongname "the German Empire". dbr:german_empire dbp-owl:capital dbr:berlin. 13

14 JSON-LD JSON (JavaScript Object Notation) is a lightweight data-interchange format that is completely language independent but uses conventions that are familiar to programmers of most of the programming languages (an object is an unordered set of name/value pairs). JSON-LD is a lightweight Linked Data format that extends JSON: easy for humans to read and write it is based on the already successful JSON format provides a way to help JSON data interoperate at Web-scale { } "@context": " "@id": " "name": "John Lennon", "born": " ", "spouse": " JSON for Linked Data: JSON-LD Playground: JSON-LD parser/serializer for RDFLib (Python lib): JSON-LD Processor and API implementation in JavaScript: 14

15 Ambiguity To be specific JSON-LD { name : Oleksiy, homepage : } { name : olkhriye, homepage : } { : Oleksiy, : } To be very concise use JSON-LD Context used to define the short-hand names : name : Oleksiy, homepage : } :{ schema : name : schema:givenname, homepage : schema:url }, name : Oleksiy, homepage : } 15

16 JSON-LD Identifiers uniquely identifies things JSON-LD { name : homepage : : name : Oleksiy, homepage : } } { name : homepage : } : name : Oleksiy, homepage : } 16

17 JSON-LD Type sets the data type of a node or typed value JSON-LD Value JSON-LD { name : homepage : } : : name : Oleksiy, homepage : } specifies the data that is associated with a particular property in the graph : : : name :[ : Oleksiy }, ], birthdate : : xsd:data } } 17

18 JSON-LD language JSON-LD specifies the language for a particular string value or the default language of a JSON-LD document { schema : name_ua : : ua }, name_ru : : ru }, birthdate : : xsd:date } : : name_ua : Oleksiy, name_ru : Aleksey, birthdate : } 18

19 JSON-LD arrays is used to set the default container type for a represents ordered collection of describes unordered set of values { "@context":{ "nick":{ "@id": " "@container": "@list }, "name":{ "@id": " "@container": "@set } }, "@id": " "nick": [ "joe", "bob", "jaybee" ], "name": [ : : en }, : : fr } ], homepage : [ ], } 19

20 JSON-LD JSON-LD reverse property allows bidirection in directed graph [ { "@id": "#john", " John }, { "@id": "#peter", " Peter", " { "@id": "#John" } }, { "@id": "#mary", " Mary", " { "@id": "#John" } } ] { "@id": "#john", " John", "@reverse": { " [ { "@id": "#peter", " Peter" }, { "@id": "#mary", " Mary" } ] } } { "@context": { "name": " "children": { "@reverse": " } }, "@id": "#john", "name": John", "children": [ { "@id": "#peter", "name": Peter" }, { "@id": "#mary", "name": Mary" } ] } 20

21 sets the base IRI against which relative IRIs are resolved { "@context": { "label": " }, "@id": "", "label": "A simple document" } { "@context": { "@base": " }, "@id": "", "label": "A simple document" expands properties and values with a common prefix IRI. If certain keys should not be expanded using the vocabulary IRI, a term can be explicitly set to null in the context. { "@context": { "@vocab": " "dbid": null }, "@id": " "@type": "Restaurant", "name": "Salsa Orchidea", "dbid": " " } 21

22 JSON-LD JSON-LD graph - used to group a set of nodes. make statements about a graph itself, rather than just a single node. { "@context": { "generatedat": { "@id": " "@type": " } }, "@id": " "generatedat": " ", "@graph": [ { "@id": " }, { "@id": " } ] } Explicit expression of default graph { "@context":, "@graph": [ { }, { } ] } [ { "@context":, }, { "@context":, } ] 22

23 JSON-LD JSON-LD compact IRI - expressing an IRI using a prefix and suffix separated by a colon (:): prefix matches a term defined within the active context; suffix does not begin with two slashes (//); if the prefix is not defined in the active context, or the suffix begins with two slashes (e.g. the value is interpreted as absolute IRI instead; if the prefix is an underscore (_), the value is interpreted as blank node identifier instead. { } "@context": { "foaf": " }, "@type": "foaf:person", "foaf:name": "Dave Longley", 23

JSON-LD RDF JSON-LD and RDF { @Context :{ schema : http://schema.org/ }, @id : http://people.com/oleksiykhriyenko, @type : schema:person, schema:name : Oleksiy, schema:knows :{ @id : http://people.

24 JSON-LD RDF JSON-LD and RDF :{ schema : : : schema:person, schema:name : Oleksiy, schema:knows : : schema:person, schema:name : John, schema:knows : } schema: < < a schema:person ; schema:name Oleksiy ; schema:knows < < a schema:person ; schema:name John ; schema:knows < 24

25 JSON-LD and HTML JSON-LD content can be easily embedded in HTML by placing it in a script element with the type attribute set to application/ld+json. <script type= application/ld+json > : : : Person, name : Oleksiy, knows : : Person, name : John, knows : } } </script> 25

26 JSON-LD and RDFa <div prefix="foaf: <ul> <li typeof="foaf:person"> <a rel="foaf:homepage" href=" property="foaf:name">bob</a> </li> <li typeof="foaf:person"> <a rel="foaf:homepage" href=" property="foaf:name">eve</a> </li> <li typeof="foaf:person"> <a rel="foaf:homepage" href=" property="foaf:name">manu</a> </li> </ul> </div> { "@context":{ "foaf": " }, "@graph":[ { "@type": "foaf:person", "foaf:homepage": " "foaf:name": "Bob" }, { "@type": "foaf:person", "foaf:homepage": " "foaf:name": "Eve" }, { "@type": "foaf:person", "foaf:homepage": " "foaf:name": "Manu"} ] } 26

27 Part 2 Semantic Annotation 27

28 Semantic Annotation Semantic Annotation enriches content with machine-processable information by linking background information to extracted concepts. These concepts, found in a document or another piece of content, are unambiguously defined and related to each other within and outside the content. ( A typical process of semantic enrichment includes: o Text Identification Text could be extracted from any form of unstructured data: articles, documents, nontextual sources such as PDF files, videos, voice recordings etc. o Text Analysis Algorithms split sentences and identify concepts, such as people, things, places, events, numbers. o Concept Extraction All recognized concepts are classified (they are defined as people, organizations, numbers etc.) and disambiguated (they are unambiguously defined according to a domain-specific knowledge base). For example, Rome is classified as a city and further disambiguated as Rome, Italy not Rome, Iowa. This is the most important stage of semantic annotation. It includes Named Entity Recognition and makes them machine-processable and understandable data pieces by linking them to a broader sets of already existing data. o Relationship Extraction The relationships between the extracted concepts are identified and interlinked with related external or internal domain knowledge. o Indexing and storing in a semantic graph database All the recognized and enriched with machinereadable data mentions of people, things, numbers etc. and the relationships between them are indexed and stored in a semantic graph database for further reference and use. Related materials:

DBpedia Spotlight DBpedia Spotlight is a tool for automatically annotating mentions of DBpedia resources in text, providing a solution for linking unstructured information sources to the Linked Open

29 DBpedia Spotlight DBpedia Spotlight is a tool for automatically annotating mentions of DBpedia resources in text, providing a solution for linking unstructured information sources to the Linked Open Data cloud through DBpedia. ( Try out DBpedia Spotlight through Web Application or Web Service endpoints: o o The Web Application is a user interface that allows you to enter text in a form and generates an HTML annotated version of the text with links to DBpedia. The Web Service endpoints provide programmatic access to the demo, allowing you to retrieve data also in XML or JSON. Demo: demo 29

30 GATE GATE (General Architecture for Text Engineering) is an open-source framework for text engineering. Started in 1996, GATE has a large developer community and can be more readily customized for text annotation in different domains and for different purposes. GATE is used worldwide to build bespoke solutions by organizations including the Press Association and National Archive. Information extraction is supported in many languages. ( There is a possibility to build a GATE processing pipeline specifically for your domain. For this we take an RDF dataset and use this to produce what is called a GATE Gazetteer, which is a list of entities in a domain and associated text labels used to refer to those entities. We can produce a gazetteer using the RDF data from chosen domain. A GATE pipeline can be run locally or uploaded to the GATE cloud. Once set up, text can be submitted and then annotated using the domain specific data. The annotated text can then be output in a format such as RDFa. ( Related materials: 30

Collect and/or process documents and social media, using freemium pre-packaged annotation services, or scale out

View and export the results in structured formats, or run your own private instance of our highly scalable GATE

31 GATE Cloud Text Analytics-as-a-Service. GATE Cloud the home of affordable text analytics solutions from the world-leading open source GATE platform. Collect and/or process documents and social media, using freemium pre-packaged annotation services, or scale out and run your own GATE pipeline on millions of documents. View and export the results in structured formats, or run your own private instance of our highly scalable GATE Mímir semantic search platform. ( Free Semantic Text Analysis APIs On-demand, Large-Scale Text Analytics Customized services and local cloud deployments Set of services on GATE Cloud: demo 31

OnTeA OnTeA (Ontology based Text Annotation) is a Pattern based Semantic Annotation Platform. OnTeA search or create semantic meta data from text or documents using pattern based approaches.

32 OnTeA OnTeA (Ontology based Text Annotation) is a Pattern based Semantic Annotation Platform. OnTeA search or create semantic meta data from text or documents using pattern based approaches. The Platform contains also graphical user interface, which shows identified objects in the text of message or text file. The Platform also analyses HTML, PDF and Word attachments. OnTeA uses two main techniques for information extraction: patterns based on regular expressions gazetteers: place names, locations, days of the weeks, etc. (now working with GATE or OntoText gazetteers). Link: 32

33 OnTeA Motivation: to extract semantic meta data from texts or documents Pattern based approach: Unstructured content is also contains some patterns to be recognized Patterns are used to extract various objects that can be transformed to ontology class individuals and their properties Text Patterns regular expressions Key - value Nokia, Oy Company: ([A-Za-z0-9]+)[, ]+(Inc Ltd Oy) Company: Nokia info@dna.fi [-_.a-z0-9]+@[-_.a-za-z0-9]+\.[a-z]{2,8} info@dna.fi Dr. Oleksiy Khriyenko Person: (Mr. Mrs. Dr.) ([A-Z][a-z]+ [A-Z][a-z]+) Person: Oleksiy Khriyenko Example: Text - Helsinki is the capital of Finland. Finland is in Europe. Location Patterns: rdfs:subclassof rdfs:subclassof Location: (in by) + (the)? *([A-Z][a-z]+) rdfs:subclassof Country: (capital of) + *([A-Z][a-z]+) City: ([A-Z][a-z]+) + (is) + (the)? + (capital of) Country City Continent: <Country> + (is in) + (the)? *([A-Z][a-z]+) Continent OnTeA key value pairs: rdf:type rdf:type Location: Europe Country: Finland City: Helsinki Continent: Europe country_finland rdf:type continent_europe city_helsinki 33

Cogito Intelligence API Cogito Intelligence API (http://www.intelligenceapi.

34 Cogito Intelligence API Cogito Intelligence API ( provides full semantic processing features text mining (with time references (alpha version), semantic reasoning and inferential entities), categorization, semantic tagging, emotions, sentiment, fact mining, writeprint, and extraction relationships between entities that developers can easily integrate into their analysis platforms and applications for faster evaluation and analysis of documents, web pages, social media data or any big data sets or real-time information streams. The API comes in both SOAP XML-based and RESTful JSON-based flavors, and the features include: 5 specific taxonomies of terms (in over 1,000 different categories) for Intelligence, Terrorism, Cyber Crime, Crime and Geographic domains A domain ontology (updated regularly) with a wide range of diverse topics, for example: weapons, crimes, cyber attacks, points of interest, chemical weapons, controlled substances, terrorist groups, critical infrastructure, world leaders, public companies and more Live Demo: 34

RDFaCE RDFaCE (RDFa Content Editor) is a Semantic content editor based on TinyMCE WYSIWYG editor. RDFaCE is an implementation for WYSIWYM (What You See Is What You Mean) concept (http://youtu.

35 RDFaCE RDFaCE (RDFa Content Editor) is a Semantic content editor based on TinyMCE WYSIWYG editor. RDFaCE is an implementation for WYSIWYM (What You See Is What You Mean) concept ( WYSIWYM aims to enable end-users to easily annotate their content using RDFa and Microdata markups based on Schema.org vocabularies. ( ( RDFaCE supports automatic content annotation employing Sindice, Swoogle and Prefix.cc APIs for resource suggestion (providing appropriate URIs for subjects, properties and namespaces) as well as using external NLP APIs (Alchemy, Extractiv, Open Calais, Ontos, Evri, Saplo, Lupedia and DBpedia spotlight). Available as a plugin for WordPress blogging platform ( RDFaCE lite is based on lite-weight version of RDFa. It supports RDFa and Microdata on rnews schema (limited to news-specific metadata: person, location, organization entities, etc.) Demos: 35

Semantator Semantator (Semantic Annotator) is a tool developed in Mayo Clinic for users to semantically annotate data of interest with respect of domain ontologies in plain text. (http://informatics.

36 Semantator Semantator (Semantic Annotator) is a tool developed in Mayo Clinic for users to semantically annotate data of interest with respect of domain ontologies in plain text. ( Semantator is implemented as a Protege plug-in that allows users to view the ontology used for annotation, and the annotation results in the same environment. Semantator provides two modes: Manual annotation. Expert can choose a document to be annotated and a domain ontology, highlight different pieces of information from the original text, and then mark which ontology concepts the information belongs to, link the instances together using the properties defined in the domain ontology. Semi-automatic annotation. Users can choose to use different automatic annotation tools such as the National Center for Biomedical Ontologies (NCBO) annotator and Mayo Clinic s Clinical Text Analysis and Knowledge Extraction System (ctakes), which are well-acknowledged tools for annotating biomedical and clinical text. Annotation results can be reviewed and modify as needed. Useful readings:

Structured Data Markup Helper Structured Data Markup Helper helps to update a site with on-page markup that enables search engines (e.g. Google, Bing, Yahoo!

37 Structured Data Markup Helper Structured Data Markup Helper helps to update a site with on-page markup that enables search engines (e.g. Google, Bing, Yahoo!, Yandex) and other products to understand the information on web pages and provide richer search results in order to make it easier for users to find relevant information on the web. ( Markup Helper uses microdata and JSON-LD formats with the schema.org vocabulary (a collaboration by Google, Microsoft, and Yahoo! to improve data description and interoperability on the web). Helpful links: About Markup Helper: Microdata: JSON-LD: Schema.org: 37

38 Cognitive Services and APIs for NLP IBM Watson NLP related services on IBM Bluemix cloud ( o o o o Natural Language Understanding Discovery Tone Analyzer Personality Insights Cogito API a Natural Language Processing API. It is a ready to deploy and fully configured API series that helps developers accelerate creation and deployment of unique applications that leverage large volumes of unstructured information from multiple sources. ( Dandelion API (Semantic Text Analytics as a service) From text to actionable data: extract meaning from unstructured text and put it in context with a simple API (including entity and keywords/concepts extraction, content classification, sentiment analysis, semantic similarity, etc.) ( Yahoo Content Analysis API lets users perform content analysis on text or a URL ( 38

Data Analytics Products Products that address today s challenges of complexity, heterogeneity and scalability to enable intelligent search, analysis and better decisions.

intuitive data visualization explore data via interactive navigation Ontos EIGER comprises a suite of modules for integrating, linking, exploring and analyzing many different data silos.

39 Data Analytics Products Products that address today s challenges of complexity, heterogeneity and scalability to enable intelligent search, analysis and better decisions. Such products try to bring meaning to data: analyze data extracted from heterogeneous sources create new knowledge via linking and fusion of data get meaning from data and answer the queries via intuitive data visualization explore data via interactive navigation Ontos EIGER comprises a suite of modules for integrating, linking, exploring and analyzing many different data silos. The back-end is based on the W3C standard, especially the linked data paradigm. The build in store serves as the enterprise knowledge graph or corporate knowledgebase. ( IBM Watson Analytics delivers cloud-based guided analytics, data visualization and predictive analytics that make understanding data easier. Watson Analytics offers a smart data discovery service available on the cloud, it guides data exploration, automates predictive analytics and enables effortless dashboard and infographic creation. ( /) IBM Watson Explorer is an cognitive search and content analysis platform that gives access to insights from all the data that can be used to drive business performance and growth; search and analyze structured, unstructured, internal, external and public content to uncover trends and patterns that improve decision-making, customer service and returnon-investment; Leverage built-in machine learning, natural language processing and nextgen APIs to unlock hidden value in ALL data. ( /) etc. 39

40 Watson Knowledge Studio Watson can be taught to extract meaningful information from unstructured text. User can create annotators that later will be used by Watson to discover relationships in unstructured data. Watson Knowledge Studio is a cloud-based application that enables developers and domain experts to collaborate and create custom annotator components for unique industries. These annotators can identify mentions and relationships in unstructured data and be easily administered throughout their lifecycle using one common tool. Annotator components can be deployed directly to IBM Watson Explorer and Alchemy Language on IBM Watson Developer Cloud. Link:

Refer Refer is an online-recommendation system based on Linked Open

It aims to improve the user s and author s experience while

archives through Automated Annotation, Semantic Analysis, Content

41 Refer Refer is an online-recommendation system based on Linked Open Data and Semantic Web Technologies. It aims to improve the user s and author s experience while curating and navigating in blogs, multimedia platforms, and archives through Automated Annotation, Semantic Analysis, Content Enrichment and Relation Browser. ( Content Analysis Exploratory Search Interactive Navigation demo 41

Fluent Editor Fluet Editor is an comprehensive tool for editing and manipulating complex ontologies that uses Controlled Natural Language (CNL): o o o o o o allows natural-language driven

Editor, it stops the user from entering any sentence that is grammatically or morphologically incorrect and actively helps the user during sentence writing; can be integrated with any other

42 Fluent Editor Fluet Editor is an comprehensive tool for editing and manipulating complex ontologies that uses Controlled Natural Language (CNL): o o o o o o allows natural-language driven ontology creation and editing; provides a more user-friendly alternative to XML-based OWL editors; uses of Controlled English as a knowledge modeling language; supported via Predictive Editor, it stops the user from entering any sentence that is grammatically or morphologically incorrect and actively helps the user during sentence writing; can be integrated with any other 3rd party tools compliant with W3C standards; interoperable with Protégé and uses R language package to access an ontology from R environment. Links: 42

43 Fluent Editor Controlled Natural Language Machine (web) processable; Understandable to humans; Restricted grammar and vocabulary; Simplifies editing and structure; Reduces ambiguity and errors; Full OWL2, SWRL and SPARQL compatibility. Embedded reasoner Constantly checks knowledge base for consistency; Generates XML automatically; Corrects on the fly, preventing errors and speeding work pace; Generates OWL files automatically. Predictive Editor Assists user with editing ontologies in real time; Ensures perfect logic quality of ontologies; Significantly increases editing speed; Provides type-ahead hints and auto correct. With Fluent Editor business users can Import Existing ontologies from OWL files; Export custom ontologies to OWL format; Export custom rules to SWRL format; Integrate custom software with CNL API; Integrate with distributed semantic knowledge databases. 43

44 Fluent Editor The same SWRL is verbalized in pure OWL/XML and Fluent Editor <Body> <ClassAtom> <Class IRI="#Consent" /> <Variable IRI="#Consent_0" /> </ClassAtom> <ClassAtom> <Class IRI="#Patient" /> <Variable IRI="#Patient_0" /> </ClassAtom> <ClassAtom> <Class IRI="#Therapy" /> <Variable IRI="#Therapy_0" /> </ClassAtom> <ObjectPropertyAtom> <ObjectProperty IRI="#isRecommendedTo" /> <Variable IRI="#Therapy_0" /> <Variable IRI="#Patient_0" /> </ObjectPropertyAtom> <ObjectPropertyAtom> <ObjectProperty IRI="#signs" /> <Variable IRI="#Patient_0" /> <Variable IRI="#Consent_0" /> </ObjectPropertyAtom> </Body> <Head> <ObjectPropertyAtom> <ObjectProperty IRI="#isAppliedTo" /> <Variable IRI="#Therapy_0" /> <Variable IRI="#Patient_0" /> </ObjectPropertyAtom> </Head> If a patient signs a consent and a therapy is-recommended-to the patient then the therapy is-applied-to the patient. Asking questions in Fluent Editor : Who-Or-What is a city that belongs-to Texas-State and has-latitude greateror-equal-to 0? Who-Or-What is a customer that livesin a city that belongs-to California- State and has-firstname equal-to 'John'? SWRL rule in Fluent Editor : or 44

45 Task 5 45

RDFa 1.1 Primer - Second Edition

RDFa 1.1 Primer - Second Edition Rich Structured Data Markup for Web Documents W3C Working Group Note 22 August 2013 This version: http://www.w3.org/tr/2013/note-rdfa-primer-20130822/ Latest published